Our analytics task is to identify, characterize, and visualize anomalous subsets of as large of a collection of network connection data as possible. We use a combination of HPC resources, advanced algorithms, and visualization techniques. To effectively and efficiently identify the salient portions of the data, we rely on a multistage workflow that includes data acquisition, summarization (feature extraction), novelty detection, and classification. Once these subsets of interest have been identified and automatically characterized, we use a stateof- the-art high-dimensional query system to extract this data for interactive visualization. Our approach is equally useful for other large-data analysis problems where it is more practical to identify interesting subsets of the data for visualization than it is to render all data elements. By reducing the size of the rendering workload, we enable highly interactive and useful visualizations.

Distributed computing is becoming commonplace in a variety of industries with health care being a particularly important one for society. The authors describe the development and deployment of TeleMed in a few health care domains. TeleMed is a 100% Java distributed application build on CORBA and OMG standards enabling the collaboration on the treatment of chronically ill patients in a secure manner over the Internet. These standards enable other systems to work interoperably with TeleMed[1] and provide transparent access to high performance distributed computing to the health care domain. The goal of wide scale integration of electronic medical records is a grand-challenge scale problem of global proportions with far-reaching social benefits.